Groebner Business Statistics 7 Ch01

Business Statistics: A Decision-Making Approach, 7e © 2008 Prentice-Hall, Inc. Chap 1-1

Business Statistics:

A Decision-Making Approach7th Edition

Chapter 1

The Where, Why, and How of

Data Collection


Chapter Goals

After completing this chapter, you should be

able to:

Describe key data collection methods

Know key definitions:

Population vs. Sample Primary vs. Secondary data types

Qualitative vs. Qualitative data Time Series vs. Cross-Sectional data

Explain the difference between descriptive and

inferential statistics

Describe different sampling methods


Descriptive statistics

Collecting, presenting, and describing data

Inferential statistics

Drawing conclusions and/or making decisions

concerning a population based only on

sample data

Tools of Business Statistics


Descriptive Statistics

Collect data

e.g., Survey, Observation,

Experiments

Present data

e.g., Charts and graphs

Characterize data

e.g., Sample mean =n

x i


Making statements about a population by

examining sample results

Sample statistics Population parameters

(known) Inference (unknown, but can

be estimated from

sample evidence)

SamplePopulation

Inferential Statistics


Inferential Statistics

Estimation

e.g., Estimate the population mean

weight using the sample mean

weight

Hypothesis Testing

e.g., Use sample evidence to test

the claim that the population mean

weight is 120 pounds

Drawing conclusions and/or making decisions concerning a population based on sample results.


Tools for Collecting Data

Data Collection Methods

Written

questionnaires

Experiments

Telephone

surveys

Direct observation and

personal interview


Survey Design Steps

Define the issue

what are the purpose and objectives of the survey?

Define the population of interest

Develop survey questions

make questions clear and unambiguous

use universally-accepted definitions

limit the number of questions


Survey Design Steps

Pre-test the survey

pilot test with a small group of participants

assess clarity and length

Determine the sample size and sampling

method

Select sample and administer the survey

(continued)


Types of Questions

Closed-end Questions Select from a short list of defined choices

Example: Major: __business __liberal arts

__science __other

Open-end Questions Respondents are free to respond with any value, words, or

statement

Example: What did you like best about this course?

Demographic Questions Questions about the respondents’ personal characteristics

Example: Gender: __Female __ Male


A Population is the set of all items or individuals of interest

Examples: All likely voters in the next election

All parts produced today

All sales receipts for November

A Sample is a subset of the population

Examples: 1000 voters selected at random for interview

A few parts selected for destructive testing

Every 100th receipt selected for audit

Populations and Samples


Key Definitions

A population is the entire collection of things

under consideration

A parameter is a summary measure computed to

describe a characteristic of the population

A sample is a portion of the population

selected for analysis

A statistic is a summary measure computed to

describe a characteristic of the sample


Population vs. Sample

a b c d

ef gh i jk l m n

o p q rs t u v w

x y z

Population Sample

b c

g i n

o r u

y


Why Sample?

Less time consuming than a census

Less costly to administer than a census

It is possible to obtain statistical results of a

sufficiently high precision based on samples.


Sampling Techniques

Convenience

Sampling Techniques

Nonstatistical Sampling

Judgment

Statistical Sampling

Simple

RandomSystematic

StratifiedCluster



Items of the sample are chosen based on

known or calculable probabilities


(Probability Sampling)

SystematicStratified ClusterSimple Random


Simple Random Sampling

Every possible sample of a given size has an

equal chance of being selected

Selection may be with replacement or without

replacement

The sample can be obtained using a table of

random numbers or computer random number

generator


Stratified Random Sampling

Divide population into subgroups (called strata)

according to some common characteristic

Select a simple random sample from each

subgroup

Combine samples from subgroups into one

Population

Divided

into 4

strata

Sample


Decide on sample size: n

Divide frame of N individuals into groups of k

individuals: k=N/n

Randomly select one individual from the 1st

group

Select every kth individual thereafter

Systematic Random Sampling

N = 64

n = 8

k = 8

First Group


Cluster Sampling

Divide population into several “clusters,” each

representative of the population

Select a simple random sample of clusters

All items in the selected clusters can be used, or items can be

chosen from a cluster using another probability sampling

technique

Population

divided into

16 clusters. Randomly selected

clusters for sample


Data Types

Data

Qualitative

(Categorical)

Quantitative

(Numerical)

Discrete Continuous

Examples:

Marital Status

Political Party

Eye Color

(Defined categories)Examples:

Number of Children

Defects per hour

(Counted items)

Examples:

Weight

Voltage

(Measured

characteristics)


Data Types

Time Series Data

Ordered data values observed over time

Cross Section Data

Data values observed at a fixed point in time


Data Types

Sales (in $1000’s)

2003 2004 2005 2006

Atlanta 435 460 475 490

Boston 320 345 375 395

Cleveland 405 390 410 395

Denver 260 270 285 280

Time

Series

Data

Cross Section

Data

Business Statistics: A Decision-Making Approach, 7e © 2008 Prentice-Hall, Inc.

Data Measurement Levels

Ratio/Interval Data

Ordinal Data

Nominal Data

Highest Level

Complete Analysis

Higher Level

Mid-level Analysis

Lowest Level

Basic Analysis

Categorical Codes

ID Numbers

Category Names

Rankings

Ordered Categories

Measurements


Chapter Summary

Reviewed key data collection methods

Introduced key definitions:

Population vs. Sample Primary vs. Secondary data types

Qualitative vs. Quanitative data Time Series vs. Cross-Sectional data

Examined descriptive vs. inferential statistics

Described different sampling techniques

Reviewed data types and measurement levels

Documents

Groebner Business Statistics 7 Ch01